*The author of this computation has been verified*

R Software Module: /rwasp_regression_trees1.wasp (opens new window with default values)

Title produced by software: Recursive Partitioning (Regression Trees)

Date of computation: Sun, 26 Dec 2010 20:37:47 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/26/t12933964118tmt5c3l3mrvrfy.htm/, Retrieved Sun, 26 Dec 2010 21:46:51 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/26/t12933964118tmt5c3l3mrvrfy.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

549 3 0 1 564 3.1 -2 1 586 2.9 -4 1 604 2.4 -4 1 601 2.4 -7 1 545 2.7 -9 1 537 2.5 -13 1 552 2.1 -8 1 563 1.9 -13 1 575 0.8 -15 1 580 0.8 -15 1 575 0.3 -15 1 558 0 -10 1 564 -0.9 -12 1 581 -1 -11 1 597 -0.7 -11 1 587 -1.7 -17 1 536 -1 -18 1 524 -0.2 -19 1.09 537 0.7 -22 1.31 536 0.6 -24 1.66 533 1.9 -24 2 528 2.1 -20 2.31 516 2.7 -25 2.75 502 3.2 -22 3.42 506 4.8 -17 3.97 518 5.5 -9 4.25 534 5.4 -11 4.25 528 5.9 -13 4.18 478 5.8 -11 4 469 5.1 -9 4 490 4.1 -7 4 493 4.4 -3 4 508 3.6 -3 4 517 3.5 -6 4 514 3.1 -4 4 510 2.9 -8 4 527 2.2 -1 4 542 1.4 -2 4 565 1.2 -2 4 555 1.3 -1 4 499 1.3 1 3.9 511 1.3 2 3.75 526 1.8 2 3.75 532 1.8 -1 3.65 549 1.8 1 3.5 561 1.7 -1 3.5 557 2.1 -8 3.39 566 2 1 3.25 588 1.7 2 3.17 620 1.9 -2 3 626 2.3 -2 2.93 620 2.4 -2 2.75 573 2.5 -2 2.64 573 2.8 -6 2.5 574 2.6 -4 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Goodness of Fit Correlation 0.8954 R-squared 0.8017 RMSE 0.5088 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 1 2.18818181818182 -1.18818181818182 2 1 2.18818181818182 -1.18818181818182 3 1 1.89027777777778 -0.890277777777778 4 1 1.89027777777778 -0.890277777777778 5 1 1.89027777777778 -0.890277777777778 6 1 1.89027777777778 -0.890277777777778 7 1 1.89027777777778 -0.890277777777778 8 1 1.89027777777778 -0.890277777777778 9 1 1.89027777777778 -0.890277777777778 10 1 1.08818181818182 -0.0881818181818181 11 1 1.08818181818182 -0.0881818181818181 12 1 1.08818181818182 -0.0881818181818181 13 1 1.08818181818182 -0.0881818181818181 14 1 1.08818181818182 -0.0881818181818181 15 1 1.08818181818182 -0.0881818181818181 16 1 1.08818181818182 -0.0881818181818181 17 1 1.08818181818182 -0.0881818181818181 18 1 1.08818181818182 -0.0881818181818181 19 1.09 2.51727272727273 -1.42727272727273 20 1.31 1.08818181818182 0.221818181818182 21 1.66 1.08818181818182 0.571818181818182 22 2 2.51727272727273 -0.517272727272727 23 2.31 2.51727272727273 -0.207272727272727 24 2.75 3.84526315789474 -1.09526315789474 25 3.42 3.84526315789474 -0.425263157894737 26 3.97 3.84526315789474 0.124736842105263 27 4.25 3.84526315789474 0.404736842105263 28 4.25 3.84526315789474 0.404736842105263 29 4.18 3.84526315789474 0.334736842105263 30 4 3.84526315789474 0.154736842105263 31 4 3.84526315789474 0.154736842105263 32 4 3.84526315789474 0.154736842105263 33 4 3.84526315789474 0.154736842105263 34 4 3.84526315789474 0.154736842105263 35 4 3.84526315789474 0.154736842105263 36 4 3.84526315789474 0.154736842105263 37 4 3.84526315789474 0.154736842105263 38 4 3.84526315789474 0.154736842105263 39 4 3.24111111111111 0.758888888888889 40 4 3.24111111111111 0.758888888888889 41 4 3.24111111111111 0.758888888888889 42 3.9 3.42444444444444 0.475555555555555 43 3.75 3.42444444444444 0.325555555555555 44 3.75 3.42444444444444 0.325555555555555 45 3.65 3.42444444444444 0.225555555555555 46 3.5 3.24111111111111 0.258888888888889 47 3.5 3.24111111111111 0.258888888888889 48 3.39 1.89027777777778 1.49972222222222 49 3.25 2.18818181818182 1.06181818181818 50 3.17 3.24111111111111 -0.0711111111111111 51 3 3.24111111111111 -0.241111111111111 52 2.93 2.18818181818182 0.741818181818182 53 2.75 2.18818181818182 0.561818181818182 54 2.64 2.18818181818182 0.451818181818182 55 2.5 1.89027777777778 0.609722222222222 56 2.5 1.89027777777778 0.609722222222222 57 2.45 1.89027777777778 0.559722222222222 58 2.25 2.18818181818182 0.061818181818182 59 2.25 2.18818181818182 0.061818181818182 60 2.21 1.89027777777778 0.319722222222222 61 2 1.89027777777778 0.109722222222222 62 2 1.89027777777778 0.109722222222222 63 2 1.89027777777778 0.109722222222222 64 2 1.89027777777778 0.109722222222222 65 2 1.89027777777778 0.109722222222222 66 2 1.89027777777778 0.109722222222222 67 2 1.89027777777778 0.109722222222222 68 2 1.89027777777778 0.109722222222222 69 2 2.18818181818182 -0.188181818181818 70 2 1.89027777777778 0.109722222222222 71 2 1.89027777777778 0.109722222222222 72 2 1.89027777777778 0.109722222222222 73 2 1.89027777777778 0.109722222222222 74 2 1.89027777777778 0.109722222222222 75 2 3.24111111111111 -1.24111111111111 76 2 2.18818181818182 -0.188181818181818 77 2 1.89027777777778 0.109722222222222 78 2 2.18818181818182 -0.188181818181818 79 2 1.89027777777778 0.109722222222222 80 2 1.89027777777778 0.109722222222222 81 2 1.89027777777778 0.109722222222222 82 2 3.24111111111111 -1.24111111111111 83 2 1.89027777777778 0.109722222222222 84 2 1.89027777777778 0.109722222222222 85 2 1.89027777777778 0.109722222222222 86 2 1.89027777777778 0.109722222222222 87 2 1.89027777777778 0.109722222222222 88 2 1.89027777777778 0.109722222222222 89 2 1.89027777777778 0.109722222222222 90 2.1 2.51727272727273 -0.417272727272727 91 2.5 2.51727272727273 -0.0172727272727271 92 2.5 2.51727272727273 -0.0172727272727271 93 2.55 2.51727272727273 0.0327272727272727 94 2.75 2.51727272727273 0.232727272727273 95 2.75 2.51727272727273 0.232727272727273 96 2.85 3.42444444444444 -0.574444444444445 97 3.25 3.42444444444444 -0.174444444444445 98 3.25 3.42444444444444 -0.174444444444445 99 3.25 3.42444444444444 -0.174444444444445 100 3.25 3.42444444444444 -0.174444444444445 101 3.25 3.42444444444444 -0.174444444444445 102 3.25 3.42444444444444 -0.174444444444445 103 3.25 3.42444444444444 -0.174444444444445 104 3.25 3.42444444444444 -0.174444444444445 105 3.25 3.84526315789474 -0.595263157894737 106 3.25 3.84526315789474 -0.595263157894737 107 3.25 3.84526315789474 -0.595263157894737 108 3.25 3.42444444444444 -0.174444444444445 109 3.39 2.51727272727273 0.872727272727273 110 3.75 2.51727272727273 1.23272727272727 111 4.03 3.42444444444444 0.605555555555556 112 4.49 3.84526315789474 0.644736842105263 113 4.5 4.391875 0.108125000000000 114 4.5 4.391875 0.108125000000000 115 4.58 4.391875 0.188125000000000 116 4.75 4.391875 0.358125 117 4.75 4.391875 0.358125 118 4.75 4.391875 0.358125 119 4.75 4.391875 0.358125 120 4.75 4.391875 0.358125 121 4.75 4.391875 0.358125 122 4.7 4.391875 0.308125000000000 123 4.5 4.391875 0.108125000000000 124 4.25 4.391875 -0.141875000000000 125 4.25 3.42444444444444 0.825555555555555 126 4.11 4.391875 -0.281874999999999 127 3.75 4.391875 -0.641875 128 3.51 4.391875 -0.881875 129 3.37 4.391875 -1.021875 130 3.21 3.42444444444444 -0.214444444444445 131 3 3.42444444444444 -0.424444444444445

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/26/t12933964118tmt5c3l3mrvrfy/29mp61293395859.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933964118tmt5c3l3mrvrfy/29mp61293395859.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933964118tmt5c3l3mrvrfy/3kdpr1293395859.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933964118tmt5c3l3mrvrfy/3kdpr1293395859.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933964118tmt5c3l3mrvrfy/4cm6c1293395859.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933964118tmt5c3l3mrvrfy/4cm6c1293395859.ps (open in new window)

Parameters (Session):

par1 = 4 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 4 ; par2 = none ; par3 = 3 ; par4 = no ;

R code (references can be found in the software module):

library(party) library(Hmisc) par1 <- as.numeric(par1) par3 <- as.numeric(par3) x <- data.frame(t(y)) is.data.frame(x) x <- x[!is.na(x[,par1]),] k <- length(x[1,]) n <- length(x[,1]) colnames(x)[par1] x[,par1] if (par2 == 'kmeans') { cl <- kmeans(x[,par1], par3) print(cl) clm <- matrix(cbind(cl$centers,1:par3),ncol=2) clm <- clm[sort.list(clm[,1]),] for (i in 1:par3) { cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='') } cl$cluster <- as.factor(cl$cluster) print(cl$cluster) x[,par1] <- cl$cluster } if (par2 == 'quantiles') { x[,par1] <- cut2(x[,par1],g=par3) } if (par2 == 'hclust') { hc <- hclust(dist(x[,par1])^2, 'cen') print(hc) memb <- cutree(hc, k = par3) dum <- c(mean(x[memb==1,par1])) for (i in 2:par3) { dum <- c(dum, mean(x[memb==i,par1])) } hcm <- matrix(cbind(dum,1:par3),ncol=2) hcm <- hcm[sort.list(hcm[,1]),] for (i in 1:par3) { memb[memb==hcm[i,2]] <- paste('C',i,sep='') } memb <- as.factor(memb) print(memb) x[,par1] <- memb } if (par2=='equal') { ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep='')) x[,par1] <- as.factor(ed) } table(x[,par1]) colnames(x) colnames(x)[par1] x[,par1] if (par2 == 'none') { m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) } load(file='createtable') if (par2 != 'none') { m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x) if (par4=='yes') { a<-table.start() a<-table.row.start(a) a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'',1,TRUE) a<-table.element(a,'Prediction (training)',par3+1,TRUE) a<-table.element(a,'Prediction (testing)',par3+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Actual',1,TRUE) for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) a<-table.element(a,'CV',1,TRUE) for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) a<-table.element(a,'CV',1,TRUE) a<-table.row.end(a) for (i in 1:10) { ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1)) m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,]) if (i==1) { m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,]) m.ct.i.actu <- x[ind==1,par1] m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,]) m.ct.x.actu <- x[ind==2,par1] } else { m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,])) m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1]) m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,])) m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1]) } } print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred)) numer <- 0 for (i in 1:par3) { print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,])) numer <- numer + m.ct.i.tab[i,i] } print(m.ct.i.cp <- numer / sum(m.ct.i.tab)) print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred)) numer <- 0 for (i in 1:par3) { print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,])) numer <- numer + m.ct.x.tab[i,i] } print(m.ct.x.cp <- numer / sum(m.ct.x.tab)) for (i in 1:par3) { a<-table.row.start(a) a<-table.element(a,paste('C',i,sep=''),1,TRUE) for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj]) a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4)) for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj]) a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4)) a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a,'Overall',1,TRUE) for (jjj in 1:par3) a<-table.element(a,'-') a<-table.element(a,round(m.ct.i.cp,4)) for (jjj in 1:par3) a<-table.element(a,'-') a<-table.element(a,round(m.ct.x.cp,4)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') } } m bitmap(file='test1.png') plot(m) dev.off() bitmap(file='test1a.png') plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response') dev.off() if (par2 == 'none') { forec <- predict(m) result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec)) colnames(result) <- c('Actuals','Forecasts','Residuals') print(result) } if (par2 != 'none') { print(cbind(as.factor(x[,par1]),predict(m))) myt <- table(as.factor(x[,par1]),predict(m)) print(myt) } bitmap(file='test2.png') if(par2=='none') { op <- par(mfrow=c(2,2)) plot(density(result$Actuals),main='Kernel Density Plot of Actuals') plot(density(result$Residuals),main='Kernel Density Plot of Residuals') plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals') plot(density(result$Forecasts),main='Kernel Density Plot of Predictions') par(op) } if(par2!='none') { plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted') } dev.off() if (par2 == 'none') { detcoef <- cor(result$Forecasts,result$Actuals) a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goodness of Fit',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Correlation',1,TRUE) a<-table.element(a,round(detcoef,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'R-squared',1,TRUE) a<-table.element(a,round(detcoef*detcoef,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'RMSE',1,TRUE) a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'#',header=TRUE) a<-table.element(a,'Actuals',header=TRUE) a<-table.element(a,'Forecasts',header=TRUE) a<-table.element(a,'Residuals',header=TRUE) a<-table.row.end(a) for (i in 1:length(result$Actuals)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,result$Actuals[i]) a<-table.element(a,result$Forecasts[i]) a<-table.element(a,result$Residuals[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') } if (par2 != 'none') { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'',1,TRUE) for (i in 1:par3) { a<-table.element(a,paste('C',i,sep=''),1,TRUE) } a<-table.row.end(a) for (i in 1:par3) { a<-table.row.start(a) a<-table.element(a,paste('C',i,sep=''),1,TRUE) for (j in 1:par3) { a<-table.element(a,myt[i,j]) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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